imsl.timeseries.auto_arima¶
- auto_arima(tpoints, series, orders, max_lag=10, delta=0.7, critical=3.0, epsilon=0.001, information_criterion=0, n_predict=0, confidence=95.0)¶
Identify outliers in a multiplicative seasonal ARIMA model.
This function automatically identifies time series outliers, determines parameters of a multiplicative seasonal \(\text{ARIMA}(p,0,q) \times (0,d,0)_s\) model and produces forecasts that incorporate the effects of outliers whose effects persist beyond the end of the series.
Parameters: tpoints : (n_obs,) array_like
Array containing the integer time points \(t_1, t_2, \ldots,t_{\text{n_obs}}\) the time series was observed. It is required that \(t_1, t_2, \ldots,t_{\text{n_obs}}\) are in strictly ascending order.
series : (n_obs,) array_like
Array containing the observed time series values \(Y_1^{\ast},Y_2^{\ast},\ldots,Y_{\text{n_obs}}^{\ast}\). This series can contain outliers and missing values. Outliers are identified by this function and missing values are identified by the time values in array tpoints. If the time interval between two consecutive time points is greater than one, i.e. \(t_{i+1}-t_i=m>1\), then m-1 missing values are assumed to exist between \(t_i\) and \(t_{i+1}\) at times \(t_i+1,t_i+2,\ldots,t_{i+1}-1\). Therefore, the gap free series is assumed to be defined for equidistant time points. Missing values are automatically estimated prior to identifying outliers and producing forecasts. Forecasts are generated for both missing and observed values.
orders : tuple of array_like
A four-tuple (p0, q0, s0, d0) of arrays containing the AR orders p, the MA orders q, the periods s and differences d, over which the search for the optimum model is conducted. All possible combinations of values in p0, q0, s0 and d0 are investigated. Entries in p0, q0 and d0 must be non-negative, whereas all periods in s0 must be strictly positive.
max_lag : int, optional
The maximum lag allowed when fitting an AR(p) model. If orders [0] = range(m) and orders [1] = [0], i.e. if an optimum AR model is searched, then max_lag is set internally to m-1, the maximum order of the AR models under consideration.
delta : float, optional
The dampening effect parameter used in the detection of a Temporary Change Outlier (TC), 0 < delta < 1.
critical : float, optional
Critical value used as a threshold for outlier detection, critical > 0.
epsilon : float, optional
Positive tolerance value controlling the accuracy of parameter estimates during outlier detection.
information_criterion : {AIC, AICC, BIC}, optional
The information criterion used for optimum model selection. Values AIC, AICC and BIC are named constants defined in module imsl.constants.
Criterion Selected Information Criterion AIC Akaike’s Information Criterion AICC Akaike’s Corrected Information Criterion BIC Bayesian Information Criterion Default: information_criterion = imsl.constants.AIC
n_predict : int, optional
The number of forecasts requested. Forecasts are made from the last observed value of the series.
confidence : float, optional
The confidence level for computing forecast confidence limits, taken from the exclusive interval (0, 100).
Returns: A named tuple with the following fields:
residual : (n,) ndarray
An array of length n = \(t_{\text{n_obs}}-t_1+1 \ge \text{n_obs}\), containing \(\hat{e}_t\), the estimates of the white noise in the outlier free original series.
residual_sigma : float
Residual standard error (RSE) of the outlier free original series.
outlier_statistics : tuple
A two-tuple of arrays containing the outlier statistics. The first array, of type int, contains the time points at which the outliers were observed. The second array, of type object, contains identifiers indicating the types of outliers observed. Outlier types fall into one of five categories:
Identifier Outlier Type IO Innovational Outlier AO Additive Outlier LS Level Shift Outlier TC Temporary Change Outlier UI Unable to Identify info_criteria_vals : tuple of float
A three-tuple, (aic, aicc, bic), containing the AIC (Akaike’s information criterion), AICC (corrected AIC) and BIC (Bayesian Information Criterion) value for the optimum model.
outfree_series : (n, 2) ndarray
An array of dimension n by 2, where n = \(t_{\text{n_obs}}-t_1+1\). The first column contains the observations from the original series, plus estimated values for any time gap. The second column contains the same values as the first column, adjusted by removing any outlier effects. In effect, the second column contains estimates of the underlying outlier-free series. If no outliers are detected, then both columns will contain identical values.
outfree_forecast : tuple
A three-tuple of arrays of length n_predict. The first array contains the forecasted values for the original outlier free series, the second the standard errors for these forecasts, and the third the psi weights of the infinite order moving average form of the model.
outlier_forecast : tuple
A three-tuple of arrays of length n_predict. The first array contains the forecasted values for the original series, the second the standard errors for these forecasts, and the third the psi weights of the infinite order moving average form of the model.
opt_model : named tuple
A named tuple describing the optimum model fitted to the outlier-free series. The fields are described in the following table:
Field name Content const model constant ar ndarray of autoregressive (AR) coefficients ma ndarray of moving average (MA) coefficients s period (seasonality) of the model d difference used in the model If d = 0, then an ARMA(p, q) or AR(p) model is fitted to the outlier-free version \(Y_t\) of the observed series \(Y_t^\ast\). If d>0, these parameters are computed for an ARMA(p, q) representation of the outlier-free, seasonally adjusted series \(Z_t=\Delta_s^d \cdot Y_t=(1-B_s)^d\cdot Y_t\), where \(B_sY_t=Y_{t-s}\) and \(s \ge 1\).
Notes
Function auto_arima determines the parameters of a multiplicative seasonal \(\text{ARIMA}(p,0,q) \times (0,d,0)_s\) model, and then uses the fitted model to identify outliers and prepare forecasts. The order of this model can be specified or automatically determined.
The \(\text{ARIMA}(p,0,q) \times (0,d,0)_s\) model handled by auto_arima has the following form:
\[\phi(B) \Delta_s^d(Y_t-\mu) = \theta(B)a_t, \; t=1,2,\ldots,n,\]where
\[\phi(B)=1-\phi_1B-\phi_2B^2-\cdots-\phi_pB^p, \theta(B)=1-\theta_1B-\theta_2B^2-\cdots-\theta_qB^q,\]\[\Delta_s^d=(1-B^s)^d\]and
\[B^kY_t=Y_{t-k}\,.\]It is assumed that all roots of \(\phi(B)\) and \(\theta(B)\) lie outside the unit circle. Clearly, if s = 1, the model reduces to the traditional ARIMA(p, d, q) model, and if d = 0, the model reduces to the traditional ARMA(p, q) model.
\(Y_t\) is the unobserved, outlier-free time series with mean \(\mu\) and white noise \(a_t\). This model is referred to as the underlying, outlier-free model. Function auto_arima does not assume that this series is observable. It assumes that the observed values might be contaminated by one or more outliers, whose effects are added to the underlying outlier-free series:
\[Y_t^{\ast} = Y_t + \text{outlier_effect}_{\;\,t}\]Outliers are classified into one of five categories (see “Outliers” below for details). Once outliers are identified, auto_arima estimates \(Y_t\), the outlier-free series representation of the data, by removing the estimated outlier effects.
Using the information about the adjusted \(\text{ARIMA}(p,0,q) \times (0,d,0)_s\) model and the removed outliers, forecasts are then prepared for the outlier-free series. Outlier effects are added to these forecasts to produce a forecast for the observed series, \(Y_t^{\ast}\). If there are no outliers, then the forecasts for the outlier-free series and the observed series will be identical.
Outliers
The algorithm of Chen and Liu ([R34]) is used to identify outliers. Both the time and classification for these outliers are returned in outlier_statistics. Outliers are classified into one of five categories based upon the standardized statistic for each outlier type. The time at which the outlier occurred is given in the first array of the two-tuple outlier_statistics. The outlier identifier returned in the second array is according to the descriptions in the following table:
Outlier Identifier Name General Description ‘IO’ Innovational Outlier Innovational outliers persist. That is, there is an initial impact at the time the outlier occurs. This effect continues in a lagged fashion with all future observations. The lag coefficients are determined by the coefficients of the underlying \(\text{ARIMA}(p,0,q)\times (0,d,0)_s\) model. ‘AO’ Additive Outlier Additive outliers do not persist. As the name implies, an additive outlier affects only the observation at the time the outlier occurs. Hence additive outliers have no effect on future forecasts. ‘LS’ Level Shift Level shift outliers persist. They have the effect of either raising or lowering the mean of the series starting at the time the outlier occurs. This shift in the mean is abrupt and permanent. ‘TC’ Temporary Change Temporary change outliers persist and are similar to level shift outliers with one major exception. Like level shift outliers, there is an abrupt change in the mean of the series at the time this outlier occurs. However, unlike level shift outliers, this shift is not permanent. The TC outlier gradually decays, eventually bringing the mean of the series back to its original value. The rate of this decay is modeled using the parameter delta. The default of delta=0.7 is the value recommended for general use by Chen and Liu. ‘UI’ Unable to Identify If an outlier is identified as the last observation, then the algorithm is unable to determine the outlier’s classification. For forecasting, a UI outlier is treated as an IO outlier. That is, its effect is lagged into the forecasts. References
[R34] (1, 2) Chen, C. and L. Liu (1993), Joint Estimation of Model Parameters and Outlier Effects in Time Series, Journal of the American Statistical Association, Vol. 88, No.421. [R35] (1, 2) Box, G., G. Jenkins and G. Reinsel (1994), Time Series Analysis : Forecasting and Control, Prentice Hall, New Jersey. Examples
Example 1:
This example uses time series D from [R35], the hourly viscosity readings of a chemical process. A group of AR(p) models is fit to the first 304 observations of this series, measured at time points t = 1 to t = 304. The optimum model is determined and a forecast is done at origin t = 304 for lead times 1 to 6. The forecasts are compared with the actual time series values which are stored in array actual.
>>> import numpy as np >>> import imsl.timeseries.auto_arima as auto_arima >>> # Values of series D at time points t=1,...,t=304 >>> x = [8.0, 8.0, 7.4, 8.0, 8.0, 8.0, 8.0, 8.8, 8.4, 8.4, 8.0, 8.2, 8.2, ... 8.2, 8.4, 8.4, 8.4, 8.6, 8.8, 8.6, 8.6, 8.6, 8.6, 8.6, 8.8, 8.9, ... 9.1, 9.5, 8.5, 8.4, 8.3, 8.2, 8.1, 8.3, 8.4, 8.7, 8.8, 8.8, 9.2, ... 9.6, 9.0, 8.8, 8.6, 8.6, 8.8, 8.8, 8.6, 8.6, 8.4, 8.3, 8.4, 8.3, ... 8.3, 8.1, 8.2, 8.3, 8.5, 8.1, 8.1, 7.9, 8.3, 8.1, 8.1, 8.1, 8.4, ... 8.7, 9.0, 9.3, 9.3, 9.5, 9.3, 9.5, 9.5, 9.5, 9.5, 9.5, 9.5, 9.9, ... 9.5, 9.7, 9.1, 9.1, 8.9, 9.3, 9.1, 9.1, 9.3, 9.5, 9.3, 9.3, 9.3, ... 9.9, 9.7, 9.1, 9.3, 9.5, 9.4, 9.0, 9.0, 8.8, 9.0, 8.8, 8.6, 8.6, ... 8.0, 8.0, 8.0, 8.0, 8.6, 8.0, 8.0, 8.0, 7.6, 8.6, 9.6, 9.6, 10.0, ... 9.4, 9.3, 9.2, 9.5, 9.5, 9.5, 9.9, 9.9, 9.5, 9.3, 9.5, 9.5, 9.1, ... 9.3, 9.5, 9.3, 9.1, 9.3, 9.1, 9.5, 9.4, 9.5, 9.6, 10.2, 9.8, 9.6, ... 9.6, 9.4, 9.4, 9.4, 9.4, 9.6, 9.6, 9.4, 9.4, 9.0, 9.4, 9.4, 9.6, ... 9.4, 9.2, 8.8, 8.8, 9.2, 9.2, 9.6, 9.6, 9.8, 9.8, 10.0, 10.0, 9.4, ... 9.8, 8.8, 8.8, 8.8, 8.8, 9.6, 9.6, 9.6, 9.2, 9.2, 9.0, 9.0, 9.0, ... 9.4, 9.0, 9.0, 9.4, 9.4, 9.6, 9.4, 9.6, 9.6, 9.6, 10.0, 10.0, 9.6, ... 9.2, 9.2, 9.2, 9.0, 9.0, 9.6, 9.8, 10.2, 10.0, 10.0, 10.0, 9.4, ... 9.2, 9.6, 9.7, 9.7, 9.8, 9.8, 9.8, 10.0, 10.0, 8.6, 9.0, 9.4, 9.4, ... 9.4, 9.4, 9.4, 9.6, 10.0, 10.0, 9.8, 9.8, 9.7, 9.6, 9.4, 9.2, 9.0, ... 9.4, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.0, 9.4, 9.4, 9.4, 9.6, 9.4, ... 9.6, 9.6, 9.8, 9.8, 9.8, 9.6, 9.2, 9.6, 9.2, 9.2, 9.6, 9.6, 9.6, ... 9.6, 9.6, 9.6, 10.0, 10.0, 10.4, 10.4, 9.8, 9.0, 9.6, 9.8, 9.6, ... 8.6, 8.0, 8.0, 8.0, 8.0, 8.4, 8.8, 8.4, 8.4, 9.0, 9.0, 9.4, 10.0, ... 10.0, 10.0, 10.2, 10.0, 10.0, 9.6, 9.0, 9.0, 8.6, 9.0, 9.6, 9.6, ... 9.0, 9.0, 8.9, 8.8, 8.7, 8.6, 8.3, 7.9] >>> # Actual values of series D at time points t=305,...,t=310 >>> actual = [8.5, 8.7, 8.9, 9.1, 9.1, 9.1] >>> col_labels = ("Lead Time", "Orig. Series", "Forecast", "Deviation", ... "Psi") >>> n_predict = 6 >>> # Define times from t=1 to t=304 >>> n_obs = len(x) >>> times = np.empty((n_obs,), dtype=np.int32) >>> for i in range(n_obs): ... times[i] = i+1 >>> # Candidate models (autoregressive models, AR[0],..., AR[5]) >>> cand_models = (range(6), [0], [1], [0]) >>> result = auto_arima(times, x, cand_models, critical=3.8, ... n_predict=n_predict) >>> print("\nAutomatic AR model selection\n") Automatic AR model selection >>> opt_model = result.opt_model >>> print("Optimum Model : p={0:d}, q={1:d}, s={2:d}, d={3:d}\n".format( ... opt_model.ar.size, opt_model.ma.size, opt_model.s, opt_model.d)) ... Optimum Model : p=1, q=0, s=1, d=0 >>> num_outliers = result.outlier_statistics[0].size >>> print("Number of outliers: {0:d}\n".format(num_outliers)) ... Number of outliers: 1 >>> print("Outlier statistics:\n") Outlier statistics: >>> print("Time point Outlier type\n") Time point Outlier type >>> stat = result.outlier_statistics >>> for i in range(num_outliers): ... print("{0:d}{1:>11s}".format(stat[0][i], stat[1][i])) ... 217 TC >>> print("\nAIC = {0:0.3f}".format(result.info_criteria_vals[0])) AIC = 678.225 >>> print("RSE = {0:0.3f}".format(result.residual_sigma)) RSE = 0.291 >>> np.set_printoptions(precision=3) >>> print("\nParameters:") Parameters: >>> print("Model constant : {0:0.3f}\n".format(opt_model.const)) ... Model constant : 1.044 >>> if (opt_model.ar.size > 0): ... print("AR parameters : " + str(opt_model.ar)) ... AR parameters : [ 0.888] >>> if (opt_model.ma.size > 0): ... print("MA parameters : " + str(opt_model.ma)) >>> print("") >>> fcast = result.outlier_forecast >>> # Print Forecast Table >>> print(" * * * Forecast Table * * *") ... * * * Forecast Table * * * >>> print("{0:9s} {1:>15s} {2:>10s} {3:>12s} {4:>9s}".format(col_labels[0], ... col_labels[1], col_labels[2], col_labels[3], col_labels[4])) ... Lead Time Orig. Series Forecast Deviation Psi >>> for i in range(n_predict): ... j = i+1 ... print("{0:9d} {1:15.4f} {2:10.4f} {3:12.4f} {4:9.4f}".format(j, ... actual[i], fcast[0][i], fcast[1][i], fcast[2][i])) ... 1 8.5000 8.0572 0.5697 0.8877 2 8.7000 8.1967 0.7618 0.7881 3 8.9000 8.3206 0.8843 0.6996 4 9.1000 8.4306 0.9699 0.6210 5 9.1000 8.5282 1.0325 0.5513 6 9.1000 8.6148 1.0792 0.4894 >>> # Put back the default options >>> np.set_printoptions()
Example 2:
This example uses the same data as Example 1, but now auto_arima seeks the optimum model under a set of ARIMA models with a possible seasonal adjustment. As a result, the unadjusted model with p = 3, q = 1, s = 1, d = 0 is chosen as optimum.
>>> import numpy as np >>> import imsl.timeseries.auto_arima as auto_arima >>> # Values of series D at time points t=1,...,t=304 >>> x = [8.0, 8.0, 7.4, 8.0, 8.0, 8.0, 8.0, 8.8, 8.4, 8.4, 8.0, 8.2, 8.2, ... 8.2, 8.4, 8.4, 8.4, 8.6, 8.8, 8.6, 8.6, 8.6, 8.6, 8.6, 8.8, 8.9, ... 9.1, 9.5, 8.5, 8.4, 8.3, 8.2, 8.1, 8.3, 8.4, 8.7, 8.8, 8.8, 9.2, ... 9.6, 9.0, 8.8, 8.6, 8.6, 8.8, 8.8, 8.6, 8.6, 8.4, 8.3, 8.4, 8.3, ... 8.3, 8.1, 8.2, 8.3, 8.5, 8.1, 8.1, 7.9, 8.3, 8.1, 8.1, 8.1, 8.4, ... 8.7, 9.0, 9.3, 9.3, 9.5, 9.3, 9.5, 9.5, 9.5, 9.5, 9.5, 9.5, 9.9, ... 9.5, 9.7, 9.1, 9.1, 8.9, 9.3, 9.1, 9.1, 9.3, 9.5, 9.3, 9.3, 9.3, ... 9.9, 9.7, 9.1, 9.3, 9.5, 9.4, 9.0, 9.0, 8.8, 9.0, 8.8, 8.6, 8.6, ... 8.0, 8.0, 8.0, 8.0, 8.6, 8.0, 8.0, 8.0, 7.6, 8.6, 9.6, 9.6, 10.0, ... 9.4, 9.3, 9.2, 9.5, 9.5, 9.5, 9.9, 9.9, 9.5, 9.3, 9.5, 9.5, 9.1, ... 9.3, 9.5, 9.3, 9.1, 9.3, 9.1, 9.5, 9.4, 9.5, 9.6, 10.2, 9.8, 9.6, ... 9.6, 9.4, 9.4, 9.4, 9.4, 9.6, 9.6, 9.4, 9.4, 9.0, 9.4, 9.4, 9.6, ... 9.4, 9.2, 8.8, 8.8, 9.2, 9.2, 9.6, 9.6, 9.8, 9.8, 10.0, 10.0, 9.4, ... 9.8, 8.8, 8.8, 8.8, 8.8, 9.6, 9.6, 9.6, 9.2, 9.2, 9.0, 9.0, 9.0, ... 9.4, 9.0, 9.0, 9.4, 9.4, 9.6, 9.4, 9.6, 9.6, 9.6, 10.0, 10.0, 9.6, ... 9.2, 9.2, 9.2, 9.0, 9.0, 9.6, 9.8, 10.2, 10.0, 10.0, 10.0, 9.4, ... 9.2, 9.6, 9.7, 9.7, 9.8, 9.8, 9.8, 10.0, 10.0, 8.6, 9.0, 9.4, 9.4, ... 9.4, 9.4, 9.4, 9.6, 10.0, 10.0, 9.8, 9.8, 9.7, 9.6, 9.4, 9.2, 9.0, ... 9.4, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.0, 9.4, 9.4, 9.4, 9.6, 9.4, ... 9.6, 9.6, 9.8, 9.8, 9.8, 9.6, 9.2, 9.6, 9.2, 9.2, 9.6, 9.6, 9.6, ... 9.6, 9.6, 9.6, 10.0, 10.0, 10.4, 10.4, 9.8, 9.0, 9.6, 9.8, 9.6, ... 8.6, 8.0, 8.0, 8.0, 8.0, 8.4, 8.8, 8.4, 8.4, 9.0, 9.0, 9.4, 10.0, ... 10.0, 10.0, 10.2, 10.0, 10.0, 9.6, 9.0, 9.0, 8.6, 9.0, 9.6, 9.6, ... 9.0, 9.0, 8.9, 8.8, 8.7, 8.6, 8.3, 7.9] >>> # Actual values of series D at time points t=305,...,t=310 >>> actual = [8.5, 8.7, 8.9, 9.1, 9.1, 9.1] >>> col_labels = ("Lead Time", "Orig. Series", "Forecast", "Deviation", ... "Psi") >>> n_predict = 6 >>> # Define times from t=1 to t=304 >>> n_obs = len(x) >>> times = np.empty((n_obs,), dtype=np.int32) >>> for i in range(n_obs): ... times[i] = i+1 >>> # Candidate models (multiplicative seasonal ARIMA models, with >>> # p0 = (0, 1, 2 ,3), q0 = (0, 1, 2, 3), s0 = (1, 2), d0 = (0, 1, 2)) >>> cand_models = (range(4), range(4), [1, 2], range(3)) >>> result = auto_arima(times, x, cand_models, critical=3.8, ... n_predict=n_predict) >>> print("\nAutomatic ARIMA model selection, differencing allowed\n") Automatic ARIMA model selection, differencing allowed >>> opt_model = result.opt_model >>> print("Optimum Model: p={0:d}, q={1:d}, s={2:d}, d={3:d}\n".format( ... opt_model.ar.size, opt_model.ma.size, opt_model.s, opt_model.d)) Optimum Model: p=3, q=1, s=1, d=0 >>> num_outliers = result.outlier_statistics[0].size >>> print("Number of outliers: {0:d}\n".format(num_outliers)) Number of outliers: 1 >>> print("Outlier statistics:") Outlier statistics: >>> print("Time point Outlier type") Time point Outlier type >>> stat = result.outlier_statistics >>> for i in range(num_outliers): ... print("{0:d}{1:>11s}".format(stat[0][i], stat[1][i])) ... 217 TC >>> print("\nAIC = {0:0.3f}".format(result.info_criteria_vals[0])) AIC = 675.886 >>> print("RSE = {0:0.3f}".format(result.residual_sigma)) RSE = 0.287 >>> np.set_printoptions(precision=3) >>> print("\nParameters:") Parameters: >>> print("Model constant : {0:0.3f}".format(opt_model.const)) Model constant : 1.893 >>> if (opt_model.ar.size > 0): ... print("AR parameters : " + str(opt_model.ar)) ... AR parameters : [ 0.184 0.641 -0.029] >>> if (opt_model.ma.size > 0): ... print("MA parameters : " + str(opt_model.ma)) ... MA parameters : [-0.743] >>> print("") >>> fcast = result.outlier_forecast >>> # Print Forecast Table >>> print(" * * * Forecast Table * * *") ... * * * Forecast Table * * * >>> print("{0:9s} {1:>15s} {2:>10s} {3:>12s} {4:>9s}".format(col_labels[0], ... col_labels[1], col_labels[2], col_labels[3], col_labels[4])) ... Lead Time Orig. Series Forecast Deviation Psi >>> for i in range(n_predict): ... j = i+1 ... print("{0:9d} {1:15.4f} {2:10.4f} {3:12.4f} {4:9.4f}".format(j, ... actual[i], fcast[0][i], fcast[1][i], fcast[2][i])) ... 1 8.5000 8.0471 0.5620 0.9274 2 8.7000 8.2004 0.7664 0.8123 3 8.9000 8.3347 0.8921 0.7153 4 9.1000 8.4534 0.9784 0.6257 5 9.1000 8.5570 1.0397 0.5504 6 9.1000 8.6483 1.0847 0.4819 >>> # Put back the default options >>> np.set_printoptions()
Example 3:
This example uses the same data as Example 2, but now the specific optimum model p = 3, q = 1, s = 1, d = 0 found in Example 2 is chosen for outlier detection and forecasting.
>>> import numpy as np >>> import imsl.timeseries.auto_arima as auto_arima >>> # Values of series D at time points t=1,...,t=304 >>> x = [8.0, 8.0, 7.4, 8.0, 8.0, 8.0, 8.0, 8.8, 8.4, 8.4, 8.0, 8.2, 8.2, ... 8.2, 8.4, 8.4, 8.4, 8.6, 8.8, 8.6, 8.6, 8.6, 8.6, 8.6, 8.8, 8.9, ... 9.1, 9.5, 8.5, 8.4, 8.3, 8.2, 8.1, 8.3, 8.4, 8.7, 8.8, 8.8, 9.2, ... 9.6, 9.0, 8.8, 8.6, 8.6, 8.8, 8.8, 8.6, 8.6, 8.4, 8.3, 8.4, 8.3, ... 8.3, 8.1, 8.2, 8.3, 8.5, 8.1, 8.1, 7.9, 8.3, 8.1, 8.1, 8.1, 8.4, ... 8.7, 9.0, 9.3, 9.3, 9.5, 9.3, 9.5, 9.5, 9.5, 9.5, 9.5, 9.5, 9.9, ... 9.5, 9.7, 9.1, 9.1, 8.9, 9.3, 9.1, 9.1, 9.3, 9.5, 9.3, 9.3, 9.3, ... 9.9, 9.7, 9.1, 9.3, 9.5, 9.4, 9.0, 9.0, 8.8, 9.0, 8.8, 8.6, 8.6, ... 8.0, 8.0, 8.0, 8.0, 8.6, 8.0, 8.0, 8.0, 7.6, 8.6, 9.6, 9.6, 10.0, ... 9.4, 9.3, 9.2, 9.5, 9.5, 9.5, 9.9, 9.9, 9.5, 9.3, 9.5, 9.5, 9.1, ... 9.3, 9.5, 9.3, 9.1, 9.3, 9.1, 9.5, 9.4, 9.5, 9.6, 10.2, 9.8, 9.6, ... 9.6, 9.4, 9.4, 9.4, 9.4, 9.6, 9.6, 9.4, 9.4, 9.0, 9.4, 9.4, 9.6, ... 9.4, 9.2, 8.8, 8.8, 9.2, 9.2, 9.6, 9.6, 9.8, 9.8, 10.0, 10.0, 9.4, ... 9.8, 8.8, 8.8, 8.8, 8.8, 9.6, 9.6, 9.6, 9.2, 9.2, 9.0, 9.0, 9.0, ... 9.4, 9.0, 9.0, 9.4, 9.4, 9.6, 9.4, 9.6, 9.6, 9.6, 10.0, 10.0, 9.6, ... 9.2, 9.2, 9.2, 9.0, 9.0, 9.6, 9.8, 10.2, 10.0, 10.0, 10.0, 9.4, ... 9.2, 9.6, 9.7, 9.7, 9.8, 9.8, 9.8, 10.0, 10.0, 8.6, 9.0, 9.4, 9.4, ... 9.4, 9.4, 9.4, 9.6, 10.0, 10.0, 9.8, 9.8, 9.7, 9.6, 9.4, 9.2, 9.0, ... 9.4, 9.6, 9.6, 9.6, 9.6, 9.6, 9.6, 9.0, 9.4, 9.4, 9.4, 9.6, 9.4, ... 9.6, 9.6, 9.8, 9.8, 9.8, 9.6, 9.2, 9.6, 9.2, 9.2, 9.6, 9.6, 9.6, ... 9.6, 9.6, 9.6, 10.0, 10.0, 10.4, 10.4, 9.8, 9.0, 9.6, 9.8, 9.6, ... 8.6, 8.0, 8.0, 8.0, 8.0, 8.4, 8.8, 8.4, 8.4, 9.0, 9.0, 9.4, 10.0, ... 10.0, 10.0, 10.2, 10.0, 10.0, 9.6, 9.0, 9.0, 8.6, 9.0, 9.6, 9.6, ... 9.0, 9.0, 8.9, 8.8, 8.7, 8.6, 8.3, 7.9] >>> # Actual values of series D at time points t=305,...,t=310 >>> actual = [8.5, 8.7, 8.9, 9.1, 9.1, 9.1] >>> col_labels = ("Lead Time", "Orig. Series", "Forecast", "Deviation", ... "Psi") >>> n_predict = 6 >>> # Define times from t=1 to t=304 >>> n_obs = len(x) >>> times = np.empty((n_obs,), dtype=np.int32) >>> for i in range(n_obs): ... times[i] = i+1 >>> # Candidate model (specific ARIMA model, with p0 = [3], q0 = [1], >>> # s0 = [1], d0 = [0]) >>> cand_models = ([3], [1], [1], [0]) >>> result = auto_arima(times, x, cand_models, critical=3.8, ... n_predict=n_predict) >>> print("\nSpecified ARIMA model\n") Specified ARIMA model >>> opt_model = result.opt_model >>> print("Optimum Model: p={0:d}, q={1:d}, s={2:d}, d={3:d}\n".format( ... opt_model.ar.size, opt_model.ma.size, opt_model.s, opt_model.d)) Optimum Model: p=3, q=1, s=1, d=0 >>> num_outliers = result.outlier_statistics[0].size >>> print("Number of outliers: {0:d}\n".format(num_outliers)) Number of outliers: 1 >>> print("Outlier statistics:") Outlier statistics: >>> print("Time point Outlier type") Time point Outlier type >>> stat = result.outlier_statistics >>> for i in range(num_outliers): ... print("{0:d}{1:>11s}".format(stat[0][i], stat[1][i])) ... 217 TC >>> print("\nAIC = {0:0.3f}".format(result.info_criteria_vals[0])) AIC = 675.886 >>> print("RSE = {0:0.3f}".format(result.residual_sigma)) RSE = 0.287 >>> np.set_printoptions(precision=3) >>> print("\nParameters:") Parameters: >>> print("Model constant : {0:0.3f}\n".format(opt_model.const)) Model constant : 1.893 >>> if (opt_model.ar.size > 0): ... print("AR parameters : " + str(opt_model.ar)) ... AR parameters : [ 0.184 0.641 -0.029] >>> if (opt_model.ma.size > 0): ... print("MA parameters : " + str(opt_model.ma)) ... MA parameters : [-0.743] >>> print("") >>> fcast = result.outlier_forecast >>> # Print Forecast Table >>> print(" * * * Forecast Table * * *") ... * * * Forecast Table * * * >>> print("{0:9s} {1:>15s} {2:>10s} {3:>12s} {4:>9s}".format(col_labels[0], ... col_labels[1], col_labels[2], col_labels[3], col_labels[4])) ... Lead Time Orig. Series Forecast Deviation Psi >>> for i in range(n_predict): ... j = i+1 ... print("{0:9d} {1:15.4f} {2:10.4f} {3:12.4f} {4:9.4f}".format(j, ... actual[i], fcast[0][i], fcast[1][i], fcast[2][i])) ... 1 8.5000 8.0471 0.5620 0.9274 2 8.7000 8.2004 0.7664 0.8123 3 8.9000 8.3347 0.8921 0.7153 4 9.1000 8.4534 0.9784 0.6257 5 9.1000 8.5570 1.0397 0.5504 6 9.1000 8.6483 1.0847 0.4819 >>> # Put back the default options >>> np.set_printoptions()